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|標題:||Recurrent neuro-fuzzy modeling and fuzzy MDPP control for flexible servomechanisms||作者:||Lin, C.S.
|關鍵字:||recurrent neuro-fuzzy model;TS fuzzy model;RLS algorithm;fuzzy MDPP;control;servomechanism;flexibility;friction;genetic algorithms;systems;friction;networks||Project:||Journal of Intelligent & Robotic Systems||期刊/報告no：:||Journal of Intelligent & Robotic Systems, Volume 38, Issue 2, Page(s) 213-235.||摘要:||
This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS ( recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP ( minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations.
|Appears in Collections:||期刊論文|
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